mlr_mod<-lm(y ~ x+x^2)
summary(mlr_mod)

## 
## Call:
## lm(formula = y ~ x + x^2)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.108 -1.393  0.748  1.490  3.776 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.8270     0.2431  -7.514 2.72e-11 ***
## x             1.4349     0.2569   5.585 2.09e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.406 on 98 degrees of freedom
## Multiple R-squared:  0.2415, Adjusted R-squared:  0.2337 
## F-statistic:  31.2 on 1 and 98 DF,  p-value: 2.09e-07

plot(x,y)

library(boot)

xy<-data.frame(x, y)

cv.error<-rep(0, 4)
for(i in 1:4){
  glm.fit<-glm(y~poly(x, i))
  cv.error[i]<-cv.glm(xy, glm.fit)$delta[1]
}
cv.error

## [1] 6.178719 0.982515 1.004089 1.025716

cvDF<-data.frame(degree=1:4, cv.error)